Overview

Dataset statistics

Number of variables26
Number of observations205
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory149.9 KiB
Average record size in memory748.5 B

Variable types

Numeric16
Text1
Categorical9

Alerts

aspiration is highly overall correlated with compressionratio and 1 other fieldsHigh correlation
boreratio is highly overall correlated with carlength and 8 other fieldsHigh correlation
carbody is highly overall correlated with doornumberHigh correlation
carheight is highly overall correlated with carlength and 3 other fieldsHigh correlation
carlength is highly overall correlated with boreratio and 9 other fieldsHigh correlation
carwidth is highly overall correlated with boreratio and 9 other fieldsHigh correlation
citympg is highly overall correlated with boreratio and 7 other fieldsHigh correlation
compressionratio is highly overall correlated with aspiration and 3 other fieldsHigh correlation
curbweight is highly overall correlated with boreratio and 8 other fieldsHigh correlation
cylindernumber is highly overall correlated with carwidth and 5 other fieldsHigh correlation
doornumber is highly overall correlated with carbody and 2 other fieldsHigh correlation
enginelocation is highly overall correlated with enginesize and 3 other fieldsHigh correlation
enginesize is highly overall correlated with boreratio and 11 other fieldsHigh correlation
enginetype is highly overall correlated with cylindernumber and 2 other fieldsHigh correlation
fuelsystem is highly overall correlated with aspiration and 2 other fieldsHigh correlation
fueltype is highly overall correlated with compressionratio and 2 other fieldsHigh correlation
highwaympg is highly overall correlated with boreratio and 9 other fieldsHigh correlation
horsepower is highly overall correlated with boreratio and 11 other fieldsHigh correlation
peakrpm is highly overall correlated with fueltypeHigh correlation
price is highly overall correlated with boreratio and 8 other fieldsHigh correlation
stroke is highly overall correlated with enginelocationHigh correlation
symboling is highly overall correlated with carheight and 2 other fieldsHigh correlation
wheelbase is highly overall correlated with boreratio and 10 other fieldsHigh correlation
fueltype is highly imbalanced (53.9%)Imbalance
enginelocation is highly imbalanced (89.0%)Imbalance
cylindernumber is highly imbalanced (57.6%)Imbalance
car_ID is uniformly distributedUniform
car_ID has unique valuesUnique
symboling has 67 (32.7%) zerosZeros

Reproduction

Analysis started2025-12-07 18:38:18.604620
Analysis finished2025-12-07 18:38:28.261073
Duration9.66 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

car_ID
Real number (ℝ)

Uniform  Unique 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103
Minimum1
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:28.292073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.2
Q152
median103
Q3154
95-th percentile194.8
Maximum205
Range204
Interquartile range (IQR)102

Descriptive statistics

Standard deviation59.322565
Coefficient of variation (CV)0.57594723
Kurtosis-1.2
Mean103
Median Absolute Deviation (MAD)51
Skewness0
Sum21115
Variance3519.1667
MonotonicityStrictly increasing
2025-12-07T14:38:28.342073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.5%
21
 
0.5%
31
 
0.5%
41
 
0.5%
51
 
0.5%
61
 
0.5%
71
 
0.5%
81
 
0.5%
91
 
0.5%
101
 
0.5%
Other values (195)195
95.1%
ValueCountFrequency (%)
11
0.5%
21
0.5%
31
0.5%
41
0.5%
51
0.5%
61
0.5%
71
0.5%
81
0.5%
91
0.5%
101
0.5%
ValueCountFrequency (%)
2051
0.5%
2041
0.5%
2031
0.5%
2021
0.5%
2011
0.5%
2001
0.5%
1991
0.5%
1981
0.5%
1971
0.5%
1961
0.5%

symboling
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83414634
Minimum-2
Maximum3
Zeros67
Zeros (%)32.7%
Negative25
Negative (%)12.2%
Memory size1.7 KiB
2025-12-07T14:38:28.379074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2453068
Coefficient of variation (CV)1.4929117
Kurtosis-0.67627136
Mean0.83414634
Median Absolute Deviation (MAD)1
Skewness0.21107227
Sum171
Variance1.5507891
MonotonicityNot monotonic
2025-12-07T14:38:28.457079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
067
32.7%
154
26.3%
232
15.6%
327
13.2%
-122
 
10.7%
-23
 
1.5%
ValueCountFrequency (%)
-23
 
1.5%
-122
 
10.7%
067
32.7%
154
26.3%
232
15.6%
327
13.2%
ValueCountFrequency (%)
327
13.2%
232
15.6%
154
26.3%
067
32.7%
-122
 
10.7%
-23
 
1.5%

CarName
Text

Distinct147
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
2025-12-07T14:38:28.594590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length31
Median length24
Mean length14.146341
Min length6

Characters and Unicode

Total characters2900
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)53.2%

Sample

1st rowalfa-romero giulia
2nd rowalfa-romero stelvio
3rd rowalfa-romero Quadrifoglio
4th rowaudi 100 ls
5th rowaudi 100ls
ValueCountFrequency (%)
toyota31
 
6.4%
nissan18
 
3.7%
mazda15
 
3.1%
mitsubishi13
 
2.7%
honda13
 
2.7%
corolla12
 
2.5%
subaru12
 
2.5%
volvo11
 
2.3%
peugeot11
 
2.3%
sw10
 
2.0%
Other values (167)342
70.1%
2025-12-07T14:38:28.778586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
285
 
9.8%
a259
 
8.9%
o243
 
8.4%
t167
 
5.8%
e158
 
5.4%
s153
 
5.3%
i147
 
5.1%
l138
 
4.8%
r133
 
4.6%
u126
 
4.3%
Other values (36)1091
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)2900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
285
 
9.8%
a259
 
8.9%
o243
 
8.4%
t167
 
5.8%
e158
 
5.4%
s153
 
5.3%
i147
 
5.1%
l138
 
4.8%
r133
 
4.6%
u126
 
4.3%
Other values (36)1091
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
285
 
9.8%
a259
 
8.9%
o243
 
8.4%
t167
 
5.8%
e158
 
5.4%
s153
 
5.3%
i147
 
5.1%
l138
 
4.8%
r133
 
4.6%
u126
 
4.3%
Other values (36)1091
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
285
 
9.8%
a259
 
8.9%
o243
 
8.4%
t167
 
5.8%
e158
 
5.4%
s153
 
5.3%
i147
 
5.1%
l138
 
4.8%
r133
 
4.6%
u126
 
4.3%
Other values (36)1091
37.6%

fueltype
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
gas
185 
diesel
20 

Length

Max length6
Median length3
Mean length3.2926829
Min length3

Characters and Unicode

Total characters675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas185
90.2%
diesel20
 
9.8%

Length

2025-12-07T14:38:28.821586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T14:38:28.850589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gas185
90.2%
diesel20
 
9.8%

Most occurring characters

ValueCountFrequency (%)
s205
30.4%
g185
27.4%
a185
27.4%
e40
 
5.9%
d20
 
3.0%
i20
 
3.0%
l20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s205
30.4%
g185
27.4%
a185
27.4%
e40
 
5.9%
d20
 
3.0%
i20
 
3.0%
l20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s205
30.4%
g185
27.4%
a185
27.4%
e40
 
5.9%
d20
 
3.0%
i20
 
3.0%
l20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s205
30.4%
g185
27.4%
a185
27.4%
e40
 
5.9%
d20
 
3.0%
i20
 
3.0%
l20
 
3.0%

aspiration
Categorical

High correlation 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
std
168 
turbo
37 

Length

Max length5
Median length3
Mean length3.3609756
Min length3

Characters and Unicode

Total characters689
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std168
82.0%
turbo37
 
18.0%

Length

2025-12-07T14:38:28.885585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T14:38:28.913585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
std168
82.0%
turbo37
 
18.0%

Most occurring characters

ValueCountFrequency (%)
t205
29.8%
s168
24.4%
d168
24.4%
u37
 
5.4%
r37
 
5.4%
b37
 
5.4%
o37
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)689
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t205
29.8%
s168
24.4%
d168
24.4%
u37
 
5.4%
r37
 
5.4%
b37
 
5.4%
o37
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)689
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t205
29.8%
s168
24.4%
d168
24.4%
u37
 
5.4%
r37
 
5.4%
b37
 
5.4%
o37
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)689
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t205
29.8%
s168
24.4%
d168
24.4%
u37
 
5.4%
r37
 
5.4%
b37
 
5.4%
o37
 
5.4%

doornumber
Categorical

High correlation 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
four
115 
two
90 

Length

Max length4
Median length4
Mean length3.5609756
Min length3

Characters and Unicode

Total characters730
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four115
56.1%
two90
43.9%

Length

2025-12-07T14:38:28.944585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T14:38:28.969587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
four115
56.1%
two90
43.9%

Most occurring characters

ValueCountFrequency (%)
o205
28.1%
f115
15.8%
u115
15.8%
r115
15.8%
t90
12.3%
w90
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o205
28.1%
f115
15.8%
u115
15.8%
r115
15.8%
t90
12.3%
w90
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o205
28.1%
f115
15.8%
u115
15.8%
r115
15.8%
t90
12.3%
w90
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o205
28.1%
f115
15.8%
u115
15.8%
r115
15.8%
t90
12.3%
w90
12.3%

carbody
Categorical

High correlation 

Distinct5
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
sedan
96 
hatchback
70 
wagon
25 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.6195122
Min length5

Characters and Unicode

Total characters1357
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan96
46.8%
hatchback70
34.1%
wagon25
 
12.2%
hardtop8
 
3.9%
convertible6
 
2.9%

Length

2025-12-07T14:38:29.002586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T14:38:29.032588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sedan96
46.8%
hatchback70
34.1%
wagon25
 
12.2%
hardtop8
 
3.9%
convertible6
 
2.9%

Most occurring characters

ValueCountFrequency (%)
a269
19.8%
h148
10.9%
c146
10.8%
n127
9.4%
e108
8.0%
d104
 
7.7%
s96
 
7.1%
t84
 
6.2%
b76
 
5.6%
k70
 
5.2%
Other values (8)129
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a269
19.8%
h148
10.9%
c146
10.8%
n127
9.4%
e108
8.0%
d104
 
7.7%
s96
 
7.1%
t84
 
6.2%
b76
 
5.6%
k70
 
5.2%
Other values (8)129
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a269
19.8%
h148
10.9%
c146
10.8%
n127
9.4%
e108
8.0%
d104
 
7.7%
s96
 
7.1%
t84
 
6.2%
b76
 
5.6%
k70
 
5.2%
Other values (8)129
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a269
19.8%
h148
10.9%
c146
10.8%
n127
9.4%
e108
8.0%
d104
 
7.7%
s96
 
7.1%
t84
 
6.2%
b76
 
5.6%
k70
 
5.2%
Other values (8)129
9.5%

drivewheel
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
fwd
120 
rwd
76 
4wd
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters615
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd120
58.5%
rwd76
37.1%
4wd9
 
4.4%

Length

2025-12-07T14:38:29.071586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T14:38:29.096587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fwd120
58.5%
rwd76
37.1%
4wd9
 
4.4%

Most occurring characters

ValueCountFrequency (%)
w205
33.3%
d205
33.3%
f120
19.5%
r76
 
12.4%
49
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w205
33.3%
d205
33.3%
f120
19.5%
r76
 
12.4%
49
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w205
33.3%
d205
33.3%
f120
19.5%
r76
 
12.4%
49
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w205
33.3%
d205
33.3%
f120
19.5%
r76
 
12.4%
49
 
1.5%

enginelocation
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
front
202 
rear
 
3

Length

Max length5
Median length5
Mean length4.9853659
Min length4

Characters and Unicode

Total characters1022
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front202
98.5%
rear3
 
1.5%

Length

2025-12-07T14:38:29.128587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T14:38:29.152586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
front202
98.5%
rear3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r208
20.4%
f202
19.8%
o202
19.8%
n202
19.8%
t202
19.8%
e3
 
0.3%
a3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1022
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r208
20.4%
f202
19.8%
o202
19.8%
n202
19.8%
t202
19.8%
e3
 
0.3%
a3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1022
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r208
20.4%
f202
19.8%
o202
19.8%
n202
19.8%
t202
19.8%
e3
 
0.3%
a3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1022
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r208
20.4%
f202
19.8%
o202
19.8%
n202
19.8%
t202
19.8%
e3
 
0.3%
a3
 
0.3%

wheelbase
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.756585
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:29.183586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.02
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0217757
Coefficient of variation (CV)0.060975941
Kurtosis1.0170389
Mean98.756585
Median Absolute Deviation (MAD)2.7
Skewness1.0502138
Sum20245.1
Variance36.261782
MonotonicityNot monotonic
2025-12-07T14:38:29.231586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.521
 
10.2%
93.720
 
9.8%
95.713
 
6.3%
96.58
 
3.9%
98.47
 
3.4%
97.37
 
3.4%
96.36
 
2.9%
107.96
 
2.9%
99.16
 
2.9%
98.86
 
2.9%
Other values (43)105
51.2%
ValueCountFrequency (%)
86.62
 
1.0%
88.41
 
0.5%
88.62
 
1.0%
89.53
 
1.5%
91.32
 
1.0%
931
 
0.5%
93.15
 
2.4%
93.31
 
0.5%
93.720
9.8%
94.31
 
0.5%
ValueCountFrequency (%)
120.91
 
0.5%
115.62
 
1.0%
114.24
2.0%
1132
 
1.0%
1121
 
0.5%
1103
1.5%
109.15
2.4%
1081
 
0.5%
107.96
2.9%
106.71
 
0.5%

carlength
Real number (ℝ)

High correlation 

Distinct75
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.04927
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:29.277586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.14
Q1166.3
median173.2
Q3183.1
95-th percentile196.36
Maximum208.1
Range67
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation12.337289
Coefficient of variation (CV)0.070883886
Kurtosis-0.082894853
Mean174.04927
Median Absolute Deviation (MAD)6.9
Skewness0.15595377
Sum35680.1
Variance152.20869
MonotonicityNot monotonic
2025-12-07T14:38:29.328597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.315
 
7.3%
188.811
 
5.4%
186.77
 
3.4%
166.37
 
3.4%
171.77
 
3.4%
177.86
 
2.9%
165.36
 
2.9%
176.26
 
2.9%
186.66
 
2.9%
1725
 
2.4%
Other values (65)129
62.9%
ValueCountFrequency (%)
141.11
 
0.5%
144.62
 
1.0%
1503
 
1.5%
155.93
 
1.5%
156.91
 
0.5%
157.11
 
0.5%
157.315
7.3%
157.91
 
0.5%
158.73
 
1.5%
158.81
 
0.5%
ValueCountFrequency (%)
208.11
 
0.5%
202.62
1.0%
199.62
1.0%
199.21
 
0.5%
198.94
2.0%
1971
 
0.5%
193.81
 
0.5%
192.73
1.5%
191.71
 
0.5%
190.92
1.0%

carwidth
Real number (ℝ)

High correlation 

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.907805
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:29.377595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.9
95-th percentile70.46
Maximum72.3
Range12
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1452039
Coefficient of variation (CV)0.032548556
Kurtosis0.70276424
Mean65.907805
Median Absolute Deviation (MAD)1.4
Skewness0.9040035
Sum13511.1
Variance4.6018996
MonotonicityNot monotonic
2025-12-07T14:38:29.425595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
63.824
 
11.7%
66.523
 
11.2%
65.415
 
7.3%
63.611
 
5.4%
64.410
 
4.9%
68.410
 
4.9%
649
 
4.4%
65.58
 
3.9%
65.27
 
3.4%
64.26
 
2.9%
Other values (34)82
40.0%
ValueCountFrequency (%)
60.31
 
0.5%
61.81
 
0.5%
62.51
 
0.5%
63.41
 
0.5%
63.611
5.4%
63.824
11.7%
63.93
 
1.5%
649
 
4.4%
64.12
 
1.0%
64.26
 
2.9%
ValueCountFrequency (%)
72.31
 
0.5%
721
 
0.5%
71.73
1.5%
71.43
1.5%
70.91
 
0.5%
70.61
 
0.5%
70.51
 
0.5%
70.33
1.5%
69.62
1.0%
68.94
2.0%

carheight
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.724878
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:29.473104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.443522
Coefficient of variation (CV)0.045482132
Kurtosis-0.44381237
Mean53.724878
Median Absolute Deviation (MAD)1.6
Skewness0.063122732
Sum11013.6
Variance5.9707996
MonotonicityNot monotonic
2025-12-07T14:38:29.524106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.814
 
6.8%
5212
 
5.9%
55.712
 
5.9%
54.510
 
4.9%
54.110
 
4.9%
55.59
 
4.4%
56.78
 
3.9%
54.38
 
3.9%
52.67
 
3.4%
51.67
 
3.4%
Other values (39)108
52.7%
ValueCountFrequency (%)
47.81
 
0.5%
48.82
 
1.0%
49.42
 
1.0%
49.64
 
2.0%
49.73
 
1.5%
50.26
2.9%
50.52
 
1.0%
50.65
 
2.4%
50.814
6.8%
511
 
0.5%
ValueCountFrequency (%)
59.82
 
1.0%
59.13
 
1.5%
58.74
2.0%
58.31
 
0.5%
57.53
 
1.5%
56.78
3.9%
56.52
 
1.0%
56.32
 
1.0%
56.23
 
1.5%
56.17
3.4%

curbweight
Real number (ℝ)

High correlation 

Distinct171
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.5659
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:29.571105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1901
Q12145
median2414
Q32935
95-th percentile3503
Maximum4066
Range2578
Interquartile range (IQR)790

Descriptive statistics

Standard deviation520.6802
Coefficient of variation (CV)0.20374361
Kurtosis-0.042853766
Mean2555.5659
Median Absolute Deviation (MAD)386
Skewness0.68139819
Sum523891
Variance271107.87
MonotonicityNot monotonic
2025-12-07T14:38:29.622105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23854
 
2.0%
19183
 
1.5%
19893
 
1.5%
22753
 
1.5%
21912
 
1.0%
21282
 
1.0%
19672
 
1.0%
19092
 
1.0%
18762
 
1.0%
23372
 
1.0%
Other values (161)180
87.8%
ValueCountFrequency (%)
14881
0.5%
17131
0.5%
18191
0.5%
18371
0.5%
18742
1.0%
18762
1.0%
18891
0.5%
18901
0.5%
19001
0.5%
19051
0.5%
ValueCountFrequency (%)
40662
1.0%
39501
0.5%
39001
0.5%
37701
0.5%
37501
0.5%
37401
0.5%
37151
0.5%
36851
0.5%
35151
0.5%
35051
0.5%

enginetype
Categorical

High correlation 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
ohc
148 
ohcf
15 
ohcv
 
13
dohc
 
12
l
 
12
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1268293
Min length1

Characters and Unicode

Total characters641
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc148
72.2%
ohcf15
 
7.3%
ohcv13
 
6.3%
dohc12
 
5.9%
l12
 
5.9%
rotor4
 
2.0%
dohcv1
 
0.5%

Length

2025-12-07T14:38:29.671107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T14:38:29.757104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ohc148
72.2%
ohcf15
 
7.3%
ohcv13
 
6.3%
dohc12
 
5.9%
l12
 
5.9%
rotor4
 
2.0%
dohcv1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o197
30.7%
h189
29.5%
c189
29.5%
f15
 
2.3%
v14
 
2.2%
d13
 
2.0%
l12
 
1.9%
r8
 
1.2%
t4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)641
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o197
30.7%
h189
29.5%
c189
29.5%
f15
 
2.3%
v14
 
2.2%
d13
 
2.0%
l12
 
1.9%
r8
 
1.2%
t4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)641
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o197
30.7%
h189
29.5%
c189
29.5%
f15
 
2.3%
v14
 
2.2%
d13
 
2.0%
l12
 
1.9%
r8
 
1.2%
t4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)641
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o197
30.7%
h189
29.5%
c189
29.5%
f15
 
2.3%
v14
 
2.2%
d13
 
2.0%
l12
 
1.9%
r8
 
1.2%
t4
 
0.6%

cylindernumber
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
four
159 
six
24 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.902439
Min length3

Characters and Unicode

Total characters800
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four159
77.6%
six24
 
11.7%
five11
 
5.4%
eight5
 
2.4%
two4
 
2.0%
twelve1
 
0.5%
three1
 
0.5%

Length

2025-12-07T14:38:29.799103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T14:38:29.833104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
four159
77.6%
six24
 
11.7%
five11
 
5.4%
eight5
 
2.4%
two4
 
2.0%
twelve1
 
0.5%
three1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f170
21.2%
o163
20.4%
r160
20.0%
u159
19.9%
i40
 
5.0%
s24
 
3.0%
x24
 
3.0%
e20
 
2.5%
v12
 
1.5%
t11
 
1.4%
Other values (4)17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f170
21.2%
o163
20.4%
r160
20.0%
u159
19.9%
i40
 
5.0%
s24
 
3.0%
x24
 
3.0%
e20
 
2.5%
v12
 
1.5%
t11
 
1.4%
Other values (4)17
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f170
21.2%
o163
20.4%
r160
20.0%
u159
19.9%
i40
 
5.0%
s24
 
3.0%
x24
 
3.0%
e20
 
2.5%
v12
 
1.5%
t11
 
1.4%
Other values (4)17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f170
21.2%
o163
20.4%
r160
20.0%
u159
19.9%
i40
 
5.0%
s24
 
3.0%
x24
 
3.0%
e20
 
2.5%
v12
 
1.5%
t11
 
1.4%
Other values (4)17
 
2.1%

enginesize
Real number (ℝ)

High correlation 

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90732
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:29.876104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median120
Q3141
95-th percentile201.2
Maximum326
Range265
Interquartile range (IQR)44

Descriptive statistics

Standard deviation41.642693
Coefficient of variation (CV)0.32813469
Kurtosis5.3056821
Mean126.90732
Median Absolute Deviation (MAD)23
Skewness1.947655
Sum26016
Variance1734.1139
MonotonicityNot monotonic
2025-12-07T14:38:29.921104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
12215
 
7.3%
9215
 
7.3%
9714
 
6.8%
9814
 
6.8%
10813
 
6.3%
11012
 
5.9%
9012
 
5.9%
1098
 
3.9%
1207
 
3.4%
1417
 
3.4%
Other values (34)88
42.9%
ValueCountFrequency (%)
611
 
0.5%
703
 
1.5%
791
 
0.5%
801
 
0.5%
9012
5.9%
915
 
2.4%
9215
7.3%
9714
6.8%
9814
6.8%
1031
 
0.5%
ValueCountFrequency (%)
3261
 
0.5%
3081
 
0.5%
3041
 
0.5%
2582
 
1.0%
2342
 
1.0%
2093
1.5%
2031
 
0.5%
1943
1.5%
1834
2.0%
1816
2.9%

fuelsystem
Categorical

High correlation 

Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
mpfi
94 
2bbl
66 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.897561
Min length3

Characters and Unicode

Total characters799
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi94
45.9%
2bbl66
32.2%
idi20
 
9.8%
1bbl11
 
5.4%
spdi9
 
4.4%
4bbl3
 
1.5%
mfi1
 
0.5%
spfi1
 
0.5%

Length

2025-12-07T14:38:29.965104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T14:38:30.001108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mpfi94
45.9%
2bbl66
32.2%
idi20
 
9.8%
1bbl11
 
5.4%
spdi9
 
4.4%
4bbl3
 
1.5%
mfi1
 
0.5%
spfi1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b160
20.0%
i145
18.1%
p104
13.0%
f96
12.0%
m95
11.9%
l80
10.0%
266
8.3%
d29
 
3.6%
111
 
1.4%
s10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)799
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b160
20.0%
i145
18.1%
p104
13.0%
f96
12.0%
m95
11.9%
l80
10.0%
266
8.3%
d29
 
3.6%
111
 
1.4%
s10
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)799
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b160
20.0%
i145
18.1%
p104
13.0%
f96
12.0%
m95
11.9%
l80
10.0%
266
8.3%
d29
 
3.6%
111
 
1.4%
s10
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)799
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b160
20.0%
i145
18.1%
p104
13.0%
f96
12.0%
m95
11.9%
l80
10.0%
266
8.3%
d29
 
3.6%
111
 
1.4%
s10
 
1.3%

boreratio
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3297561
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:30.045105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.27084371
Coefficient of variation (CV)0.081340404
Kurtosis-0.78504183
Mean3.3297561
Median Absolute Deviation (MAD)0.26
Skewness0.020156418
Sum682.6
Variance0.073356313
MonotonicityNot monotonic
2025-12-07T14:38:30.088106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.6223
 
11.2%
3.1920
 
9.8%
3.1515
 
7.3%
2.9712
 
5.9%
3.0312
 
5.9%
3.469
 
4.4%
3.318
 
3.9%
3.438
 
3.9%
3.788
 
3.9%
2.917
 
3.4%
Other values (28)83
40.5%
ValueCountFrequency (%)
2.541
 
0.5%
2.681
 
0.5%
2.917
3.4%
2.921
 
0.5%
2.9712
5.9%
2.991
 
0.5%
3.015
2.4%
3.0312
5.9%
3.056
2.9%
3.081
 
0.5%
ValueCountFrequency (%)
3.942
 
1.0%
3.82
 
1.0%
3.788
 
3.9%
3.761
 
0.5%
3.743
 
1.5%
3.75
 
2.4%
3.632
 
1.0%
3.6223
11.2%
3.611
 
0.5%
3.61
 
0.5%

stroke
Real number (ℝ)

High correlation 

Distinct37
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2554146
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:30.131107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31359701
Coefficient of variation (CV)0.096330898
Kurtosis2.1743964
Mean3.2554146
Median Absolute Deviation (MAD)0.14
Skewness-0.68970458
Sum667.36
Variance0.098343087
MonotonicityNot monotonic
2025-12-07T14:38:30.172106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
3.420
 
9.8%
3.2314
 
6.8%
3.1514
 
6.8%
3.0314
 
6.8%
3.3913
 
6.3%
2.6411
 
5.4%
3.359
 
4.4%
3.299
 
4.4%
3.468
 
3.9%
3.586
 
2.9%
Other values (27)87
42.4%
ValueCountFrequency (%)
2.071
 
0.5%
2.192
 
1.0%
2.361
 
0.5%
2.6411
5.4%
2.682
 
1.0%
2.761
 
0.5%
2.82
 
1.0%
2.871
 
0.5%
2.93
 
1.5%
3.0314
6.8%
ValueCountFrequency (%)
4.172
 
1.0%
3.93
 
1.5%
3.864
2.0%
3.645
2.4%
3.586
2.9%
3.544
2.0%
3.525
2.4%
3.56
2.9%
3.474
2.0%
3.468
3.9%

compressionratio
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142537
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:30.209106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.82
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9720403
Coefficient of variation (CV)0.39162199
Kurtosis5.2330543
Mean10.142537
Median Absolute Deviation (MAD)0.4
Skewness2.6108625
Sum2079.22
Variance15.777104
MonotonicityNot monotonic
2025-12-07T14:38:30.248106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
946
22.4%
9.426
12.7%
8.514
 
6.8%
9.513
 
6.3%
9.311
 
5.4%
8.79
 
4.4%
9.28
 
3.9%
88
 
3.9%
77
 
3.4%
235
 
2.4%
Other values (22)58
28.3%
ValueCountFrequency (%)
77
3.4%
7.55
 
2.4%
7.64
 
2.0%
7.72
 
1.0%
7.81
 
0.5%
88
3.9%
8.12
 
1.0%
8.33
 
1.5%
8.45
 
2.4%
8.514
6.8%
ValueCountFrequency (%)
235
2.4%
22.71
 
0.5%
22.53
1.5%
221
 
0.5%
21.91
 
0.5%
21.54
2.0%
215
2.4%
11.51
 
0.5%
10.11
 
0.5%
103
1.5%

horsepower
Real number (ℝ)

High correlation 

Distinct59
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.11707
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:30.292105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile180.8
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.544167
Coefficient of variation (CV)0.37980483
Kurtosis2.6840062
Mean104.11707
Median Absolute Deviation (MAD)25
Skewness1.4053102
Sum21344
Variance1563.7411
MonotonicityNot monotonic
2025-12-07T14:38:30.338105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6819
 
9.3%
7011
 
5.4%
6910
 
4.9%
1169
 
4.4%
1108
 
3.9%
957
 
3.4%
1606
 
2.9%
626
 
2.9%
886
 
2.9%
1016
 
2.9%
Other values (49)117
57.1%
ValueCountFrequency (%)
481
 
0.5%
522
 
1.0%
551
 
0.5%
562
 
1.0%
581
 
0.5%
601
 
0.5%
626
 
2.9%
641
 
0.5%
6819
9.3%
6910
4.9%
ValueCountFrequency (%)
2881
 
0.5%
2621
 
0.5%
2073
1.5%
2001
 
0.5%
1842
1.0%
1823
1.5%
1762
1.0%
1751
 
0.5%
1622
1.0%
1612
1.0%

peakrpm
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5125.122
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:30.376105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5980
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation476.98564
Coefficient of variation (CV)0.093068155
Kurtosis0.086755856
Mean5125.122
Median Absolute Deviation (MAD)300
Skewness0.075158722
Sum1050650
Variance227515.3
MonotonicityNot monotonic
2025-12-07T14:38:30.413106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
550037
18.0%
480036
17.6%
500027
13.2%
520023
11.2%
540013
 
6.3%
60009
 
4.4%
58007
 
3.4%
45007
 
3.4%
52507
 
3.4%
41505
 
2.4%
Other values (13)34
16.6%
ValueCountFrequency (%)
41505
 
2.4%
42005
 
2.4%
42503
 
1.5%
43504
 
2.0%
44003
 
1.5%
45007
 
3.4%
46501
 
0.5%
47504
 
2.0%
480036
17.6%
49001
 
0.5%
ValueCountFrequency (%)
66002
 
1.0%
60009
 
4.4%
59003
 
1.5%
58007
 
3.4%
57501
 
0.5%
56001
 
0.5%
550037
18.0%
540013
 
6.3%
53001
 
0.5%
52507
 
3.4%

citympg
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.219512
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:30.451106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5421417
Coefficient of variation (CV)0.25940794
Kurtosis0.57864834
Mean25.219512
Median Absolute Deviation (MAD)5
Skewness0.66370403
Sum5170
Variance42.799617
MonotonicityNot monotonic
2025-12-07T14:38:30.490622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3128
13.7%
1927
13.2%
2422
10.7%
2714
 
6.8%
1713
 
6.3%
2612
 
5.9%
2312
 
5.9%
218
 
3.9%
258
 
3.9%
308
 
3.9%
Other values (19)53
25.9%
ValueCountFrequency (%)
131
 
0.5%
142
 
1.0%
153
 
1.5%
166
 
2.9%
1713
6.3%
183
 
1.5%
1927
13.2%
203
 
1.5%
218
 
3.9%
224
 
2.0%
ValueCountFrequency (%)
491
 
0.5%
471
 
0.5%
451
 
0.5%
387
3.4%
376
2.9%
361
 
0.5%
351
 
0.5%
341
 
0.5%
331
 
0.5%
321
 
0.5%

highwaympg
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.75122
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:30.531619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42.8
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8864431
Coefficient of variation (CV)0.22394049
Kurtosis0.44007038
Mean30.75122
Median Absolute Deviation (MAD)5
Skewness0.53999719
Sum6304
Variance47.423099
MonotonicityNot monotonic
2025-12-07T14:38:30.571618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2519
 
9.3%
2417
 
8.3%
3817
 
8.3%
3016
 
7.8%
3216
 
7.8%
3414
 
6.8%
2813
 
6.3%
3713
 
6.3%
2910
 
4.9%
339
 
4.4%
Other values (20)61
29.8%
ValueCountFrequency (%)
162
 
1.0%
171
 
0.5%
182
 
1.0%
192
 
1.0%
202
 
1.0%
228
3.9%
237
 
3.4%
2417
8.3%
2519
9.3%
263
 
1.5%
ValueCountFrequency (%)
541
 
0.5%
531
 
0.5%
501
 
0.5%
472
 
1.0%
462
 
1.0%
434
 
2.0%
423
 
1.5%
413
 
1.5%
392
 
1.0%
3817
8.3%

price
Real number (ℝ)

High correlation 

Distinct189
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13276.711
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-07T14:38:30.614618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6197
Q17788
median10295
Q316503
95-th percentile32472.4
Maximum45400
Range40282
Interquartile range (IQR)8715

Descriptive statistics

Standard deviation7988.8523
Coefficient of variation (CV)0.60171925
Kurtosis3.0516479
Mean13276.711
Median Absolute Deviation (MAD)3306
Skewness1.7776782
Sum2721725.7
Variance63821762
MonotonicityNot monotonic
2025-12-07T14:38:30.662618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165002
 
1.0%
89212
 
1.0%
62292
 
1.0%
66922
 
1.0%
55722
 
1.0%
79572
 
1.0%
76092
 
1.0%
88452
 
1.0%
72952
 
1.0%
84952
 
1.0%
Other values (179)185
90.2%
ValueCountFrequency (%)
51181
0.5%
51511
0.5%
51951
0.5%
53481
0.5%
53891
0.5%
53991
0.5%
54991
0.5%
55722
1.0%
60951
0.5%
61891
0.5%
ValueCountFrequency (%)
454001
0.5%
413151
0.5%
409601
0.5%
370281
0.5%
368801
0.5%
360001
0.5%
355501
0.5%
350561
0.5%
341841
0.5%
340281
0.5%

Interactions

2025-12-07T14:38:27.500018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.093041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.658658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.209120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.766125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.375917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.908440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.497247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.025029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.594713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.148770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.653396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.259434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.777946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.337945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.885477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.538019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.133039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.692658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.244530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.803126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.409917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.944440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.531417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.058030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.631709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.181771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.691397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.293434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.810946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.373946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.922477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.573020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.167039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.721658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.277611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.837126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.441917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.976440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.561419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.089030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.665709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.210767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.723396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.322436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.839946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.405948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.956475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.610019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.202039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.755658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.312616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.873125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.476439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.011440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.595420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.122030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.701709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.243765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.760396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.355435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.872946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.440948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.994508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.647018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.238039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.831120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.348612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.908126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.510439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.044439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.629416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.156030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.736709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.276766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.843401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.389434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.904945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.476462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.030508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.683017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.273039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.862119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.381612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.943387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.541438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.077440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.662421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.188030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.770713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.307765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.883400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.425433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.936946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.510460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.064507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.720017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.307039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.893121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.416611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.027386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.574440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.109439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.695417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.220035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.804713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.338766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.917398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.458436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.968947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.543460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.099508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.761244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.343040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.924119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.450615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.062387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.607440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.143439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.726416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.253030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.838711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.369768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.951399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.489946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.999945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.577459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.133507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.797245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.377041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.955122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.485125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.095386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.640440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.225438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.759030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.283030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.873769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.400766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.984398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.520946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.030945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.609474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.168511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.835243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.413039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.987120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.521125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.133388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.674439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.258439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.792030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.317030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.908766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.431770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.020398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.552946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.062946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.647474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.205507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.875243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.445039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.016120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.554126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.165917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.704439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.289439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.824032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.347029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.939768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.460773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.052399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.583946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.093946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.678475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.238508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.912243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.481658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.048120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.590126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.200917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.739439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.324112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.858031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.381197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.974767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.493396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.086400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.616946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.126945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.714475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.275508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.948245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.513659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.079120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.624128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.233917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.771440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.358111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.891031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.412197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.006770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.523401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.118398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.645946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.156945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.747475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.310509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.982243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.547660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.110120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.657125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.266917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.803440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.388113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.922034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.444197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.040767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.552400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.151398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.675946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.237945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.779477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.344511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:28.019243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.582659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.142121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.692126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.301917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.836440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.422110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.956034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.525709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.074766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.584396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.185397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.707947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.269945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.812475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.378508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:28.056243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:19.619658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.175120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:20.728125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.338917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:21.872439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.457114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:22.989032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:23.559709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.111767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:24.618397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.222396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:25.742947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.303945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:26.847475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T14:38:27.461511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-07T14:38:30.757848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
aspirationboreratiocar_IDcarbodycarheightcarlengthcarwidthcitympgcompressionratiocurbweightcylindernumberdoornumberdrivewheelenginelocationenginesizeenginetypefuelsystemfueltypehighwaympghorsepowerpeakrpmpricestrokesymbolingwheelbase
aspiration1.0000.3350.2610.0000.2370.2070.3010.1860.5540.3750.1960.0000.1180.0000.2710.1500.6100.3740.3190.3430.3110.4070.2650.1850.310
boreratio0.3351.0000.2730.1510.2160.6390.610-0.609-0.1600.7020.2580.1630.4340.3270.7010.4180.3450.168-0.6150.639-0.2980.644-0.083-0.1700.537
car_ID0.2610.2731.0000.1770.2630.1550.1490.0560.1510.1240.2790.3430.4240.3060.0890.4120.3850.2890.0210.005-0.2300.020-0.160-0.1570.197
carbody0.0000.1510.1771.0000.4970.2410.1280.0000.0480.2300.0680.7410.2140.4380.2020.1320.1440.1730.0000.1890.0740.2290.1510.3340.334
carheight0.2370.2160.2630.4971.0000.5250.350-0.0690.0000.3460.3500.5410.3600.2720.2000.3880.2920.277-0.1330.011-0.2960.243-0.018-0.5230.633
carlength0.2070.6390.1550.2410.5251.0000.888-0.670-0.1930.8900.3560.3650.4090.0000.7830.3170.3260.110-0.6980.661-0.2690.8040.187-0.3960.912
carwidth0.3010.6100.1490.1280.3500.8881.000-0.688-0.1460.8640.5670.3050.4030.1600.7710.3690.2460.233-0.7010.689-0.1990.8110.240-0.2540.812
citympg0.186-0.6090.0560.000-0.069-0.670-0.6881.0000.479-0.8130.4240.0030.3800.110-0.7300.2090.3040.3890.968-0.911-0.131-0.829-0.030-0.018-0.493
compressionratio0.554-0.1600.1510.0480.000-0.193-0.1460.4791.000-0.2190.5210.1860.1140.000-0.2350.3380.5180.9930.445-0.353-0.022-0.174-0.0700.023-0.126
curbweight0.3750.7020.1240.2300.3460.8900.864-0.813-0.2191.0000.4820.2740.4560.1000.8780.3270.2920.305-0.8340.808-0.2360.9090.163-0.2560.765
cylindernumber0.1960.2580.2790.0680.3500.3560.5670.4240.5210.4821.0000.1340.3360.2880.6420.5460.3730.1550.5000.5640.2830.4290.2390.1600.316
doornumber0.0000.1630.3430.7410.5410.3650.3050.0030.1860.2740.1341.0000.0500.0670.2070.2000.2450.1610.1190.1710.2440.0000.1320.6840.445
drivewheel0.1180.4340.4240.2140.3600.4090.4030.3800.1140.4560.3360.0501.0000.1240.4690.4250.3870.0880.4370.4020.2420.4510.3380.2660.417
enginelocation0.0000.3270.3060.4380.2720.0000.1600.1100.0000.1000.2880.0670.1241.0000.6190.3990.0000.0000.1010.8430.4480.4510.6150.2720.568
enginesize0.2710.7010.0890.2020.2000.7830.771-0.730-0.2350.8780.6420.2070.4690.6191.0000.5270.3330.157-0.7210.817-0.2730.8260.292-0.1770.648
enginetype0.1500.4180.4120.1320.3880.3170.3690.2090.3380.3270.5460.2000.4250.3990.5271.0000.3770.2500.3250.5140.3590.2880.4040.2220.353
fuelsystem0.6100.3450.3850.1440.2920.3260.2460.3040.5180.2920.3730.2450.3870.0000.3330.3771.0000.9850.3410.3170.3630.2900.3030.2660.226
fueltype0.3740.1680.2890.1730.2770.1100.2330.3890.9930.3050.1550.1610.0880.0000.1570.2500.9851.0000.3360.2190.5940.3380.3750.2170.341
highwaympg0.319-0.6150.0210.000-0.133-0.698-0.7010.9680.445-0.8340.5000.1190.4370.101-0.7210.3250.3410.3361.000-0.886-0.057-0.823-0.0300.053-0.539
horsepower0.3430.6390.0050.1890.0110.6610.689-0.911-0.3530.8080.5640.1710.4020.8430.8170.5140.3170.219-0.8861.0000.1130.8550.130-0.0100.505
peakrpm0.311-0.298-0.2300.074-0.296-0.269-0.199-0.131-0.022-0.2360.2830.2440.2420.448-0.2730.3590.3630.594-0.0570.1131.000-0.066-0.0740.282-0.312
price0.4070.6440.0200.2290.2430.8040.811-0.829-0.1740.9090.4290.0000.4510.4510.8260.2880.2900.338-0.8230.855-0.0661.0000.111-0.1450.682
stroke0.265-0.083-0.1600.151-0.0180.1870.240-0.030-0.0700.1630.2390.1320.3380.6150.2920.4040.3030.375-0.0300.130-0.0740.1111.000-0.0190.227
symboling0.185-0.170-0.1570.334-0.523-0.396-0.254-0.0180.023-0.2560.1600.6840.2660.272-0.1770.2220.2660.2170.053-0.0100.282-0.145-0.0191.000-0.538
wheelbase0.3100.5370.1970.3340.6330.9120.812-0.493-0.1260.7650.3160.4450.4170.5680.6480.3530.2260.341-0.5390.505-0.3120.6820.227-0.5381.000

Missing values

2025-12-07T14:38:28.127243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-07T14:38:28.209146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

car_IDsymbolingCarNamefueltypeaspirationdoornumbercarbodydrivewheelenginelocationwheelbasecarlengthcarwidthcarheightcurbweightenginetypecylindernumberenginesizefuelsystemboreratiostrokecompressionratiohorsepowerpeakrpmcitympghighwaympgprice
013alfa-romero giuliagasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212713495.000
123alfa-romero stelviogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212716500.000
231alfa-romero Quadrifogliogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.01545000192616500.000
342audi 100 lsgasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.01025500243013950.000
452audi 100lsgasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.01155500182217450.000
562audi foxgasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.51105500192515250.000
671audi 100lsgasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.51105500192517710.000
781audi 5000gasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.51105500192518920.000
891audi 4000gasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.31405500172023875.000
9100audi 5000s (diesel)gasturbotwohatchback4wdfront99.5178.267.952.03053ohcfive131mpfi3.133.407.01605500162217859.167
car_IDsymbolingCarNamefueltypeaspirationdoornumbercarbodydrivewheelenginelocationwheelbasecarlengthcarwidthcarheightcurbweightenginetypecylindernumberenginesizefuelsystemboreratiostrokecompressionratiohorsepowerpeakrpmcitympghighwaympgprice
195196-1volvo 144eagasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.51145400232813415.0
196197-2volvo 244dlgasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.51145400242815985.0
197198-1volvo 245gasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.51145400242816515.0
198199-2volvo 264glgasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.51625100172218420.0
199200-1volvo dieselgasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.51625100172218950.0
200201-1volvo 145e (sw)gasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.51145400232816845.0
201202-1volvo 144eagasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.71605300192519045.0
202203-1volvo 244dlgasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.81345500182321485.0
203204-1volvo 246dieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.01064800262722470.0
204205-1volvo 264glgasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.51145400192522625.0